Making the move from a tactical to a strategic supply chain

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1 a tactical to a strategic

2 Top five analytics Supply chain analytics appears to be a poorly understood technology in dire need of some best practices. Supply chain analytics and manufacturing BI raise cultural and organizational issues that strongly affect how companies apply the technology. There are different approaches that companies can take when choosing their. In this E-Guide, readers will hear from experts around the different analytics options and what may work best in certain environments. Top five integration challenges There are numerous challenges that organizations face when implementing technology. Here are the biggest integration mistakes companies make: 1. Failure to secure top-management sponsorship and leadership. Since processes impact so much of a company's mission critical value-chain, securing top management sponsorship and leadership is critical for cross-functional commitment, driving enterprise-wide accountability, or allocating proper resources to insure a successful implementation. One tried-and-true solution is creating an executive steering committee to ensure that resources are aligned and obstacles are removed. 2. Wrong Process, worst practice. Prior to an implementation, it is critical that companies conduct research to insure that the new technology is supported by the right process and is grounded in cross-industry best practices related to that process. The ultimate benefits of a technology are best gained by talking with others who have successfully applied such technology within their s. Many technology providers can provide information on best Page 2 of 8

3 practices or referrals to those companies that have gained significant benefits in utilizing the technology. Top five analytics 3. Inaccurate and out-of-date information. Timely data and information are what successful s are all about. Many of the initial benefits of technology automation have been neutered by the existence of inaccurate or untimely data. Before implementing any new software, the implementation team must first dedicate efforts to make sure that all data related in any way to the application is clean and accurate. The adage of "garbage-in and garbage-out" has a special meaning in management technology. 4. No cross-functional representation. A broad-based team that is empowered to make decisions about planning and implementation will insure a successful deployment. This team should include the ultimate stakeholders in use of the new technology, and all those who will benefit with improved process flow and information. Systems integration is a critical requirement in most technology. It is essential that the implementation team have IT representation to lead the systems integration effort and address needs for inbound and outbound data or new integration and information reporting needs. 5. Broad scope. While a long-term end-state is the goal, do not attempt to implement everything at once. Apply the Pareto principle of impact. Focus on core needs of the process being automated and on scope that can deliver the initial benefits of an implementation. Celebrate short-term wins and incremental benefits. A limited focus on quick initial benefits from the implementation and sustaining value in subsequent future functionality releases can help in obtaining longer-term commitment to automation and change. About the Author: Bob Ferrari is the Managing Director of the Ferrari Consulting and Research Group LLC, a business process and technology consultant firm, Page 3 of 8

4 and is the creator and Executive Editor of the Supply Chain Matters internet blog. Top five analytics analytics Supply chain analytics and manufacturing BI raise cultural and organizational issues that strongly affect how companies apply the technology, according to James Kobielus, senior analyst at Forrester Research Inc., based in Cambridge, Mass. Decide what kind of organization you actually are, Kobielus advised. Are you an organization that goes by the numbers, or are you more qualitative? If you re more the latter, analytics is not necessarily something you go deep into. Manufacturers can vary widely in their need to make analytics available throughout the enterprise. Some may have departments like marketing or human resources running analytics; others might want to limit analytics use to the chief financial officer and highly trained statisticians in operations, for example. Figure out which are the most appropriate groups in the company to roll out the analytics tools to, Kobielus said, then categorize them as either casual users or power users. Figure out who your core users will be, and what specifically do they need. Some just need reports and dashboards. Power users, on the other hand, need advanced tools such as what-if scenarios and predictive analytics. Quite often, these are people who are involved in analyzing different scenarios, and they re often at the corporate level, where potential partnerships and acquisitions are evaluated, he said. The next step is to decide how deeply into the data the different user groups need to go. Once you ve established all that, figure out how you re going to empower their needs, Kobielus said. Casual users can be satisfied with an easy-to-use, self-service BI tool that runs on ERP or another enterprise Page 4 of 8

5 application, but analysts in the operations research department will still need their high-end analytics suites. Top five analytics Once the user application has been selected, you still need the right plumbing underneath, Kobielus said. That means establishing an analytics database with a schema on top to match, harmonize and cleanse the data. There should also be data-governance policies to ensure the trustworthiness of back-end data sources. The cost of all these infrastructure improvements can quickly get out of hand, which is why companies should carefully scope out lowest-cost solutions. More and more of the BI that we see out there in small to midmarket companies is being provided by outsourcers, Kobielus said. Such Software as a Service (SaaS) providers often provide the prebuilt integration to supply chain partners and data sources that can quickly eat up IT budgets when handled in-house. You get a good, solid, basic BI environment that probably serves 90% of your needs, he said. While more companies seem to be outsourcing their BI and using selfservice tools, that s not the case for those with highly specialized needs, according to Kobielus. A lot of the nouveau stuff like predictive analytics is being left in-house because it s being used by a small cadre of users, he said. Kobielus said some SaaS offerings that specialize in vertical industries provide another means of inculcating best practices without having to build them up internally an effort typically beyond the resources of small manufacturers. They know their core businesses, but they don t know anything about building statistical models, he said. The outsourcer is essentially a center of excellence. Inventory management and logistics are common specialties of SaaS analytics providers that are geared to manufacturers. Kobielus said he and Forrester analyst Boris Evelson researched the benefits of forming a business solutions center (BISC) to cultivate BI best Page 5 of 8

6 Top five analytics practices. They said a BISC goes beyond similar approaches such as BI competency centers and centers of excellence by being governed by the business side rather than IT, and by focusing on business solutions instead of data warehouses and other infrastructure components. Implementing a companywide analytics can prompt negative reactions from some quarters, according to Kobielus. He advised proponents to be ready to answer skeptics who will ask why existing tools aren t adequate and to take steps to address resistance from analytics specialists, who are accustomed to working independently in departmental silos. There will be cultural pushback from those teams, Kobielus said, and they might react negatively to sharing expertise with other analysts or outsourcing their skills to a service provider. Ventana Research recommends the following best practices for companies looking to improve their use of BI and analytics: 1. Assess your maturity level by comparing your company s use of BI and analytics against that of companies that use the technology innovatively and strategically, rather than just tactically. 2. Expand the use of BI and analytics to more departments and set up cross-functional teams to determine which metrics are most useful to everyone. 3. Measure the performance of business processes. 4. Use BI to improve the company s balanced scorecard results against industry benchmarks such as the Supply Chain Council s Supply Chain Operations Reference (SCOR) framework. 5. Be sure to integrate all time-phased data that exists in plans, forecasts and schedules. 6. Begin using more advanced BI technologies, including ad hoc searches, multidimensional cubes, business activity monitoring (BAM) and alerts. Page 6 of 8

7 7. Automate the exchange of information with trading partners. Top five analytics Page 7 of 8

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